{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cd5f456b",
   "metadata": {},
   "source": [
    "### Figures 3 and 4 (GTATE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9efd3531",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data loaded. Shape: (1077992, 26)\n",
      "   challenger  target    year  dispute  ptargdef  pchalally  pchaloff  \\\n",
      "0        20.0     2.0  1920.0      0.0       0.0        0.0       0.0   \n",
      "1        20.0     2.0  1921.0      0.0       0.0        0.0       0.0   \n",
      "2        20.0     2.0  1922.0      0.0       0.0        0.0       0.0   \n",
      "3        20.0     2.0  1923.0      0.0       0.0        0.0       0.0   \n",
      "4        20.0     2.0  1924.0      0.0       0.0        0.0       0.0   \n",
      "\n",
      "   pchalneu   capprop  ln_distance  ...  contig   id  cold_war  majorpower  \\\n",
      "0       0.0  0.033794     6.120297  ...     1.0  1.0       0.0         1.0   \n",
      "1       0.0  0.039966     6.120297  ...     1.0  1.0       0.0         1.0   \n",
      "2       0.0  0.031848     6.120297  ...     1.0  1.0       0.0         1.0   \n",
      "3       0.0  0.034976     6.120297  ...     1.0  1.0       0.0         1.0   \n",
      "4       0.0  0.033888     6.120297  ...     1.0  1.0       0.0         1.0   \n",
      "\n",
      "   poor_rep_t_def  poor_rep_t_all  dispute_hh3  dispute_hh4  \\\n",
      "0             0.0             0.0          0.0          0.0   \n",
      "1             0.0             0.0          0.0          0.0   \n",
      "2             0.0             0.0          0.0          0.0   \n",
      "3             0.0             0.0          0.0          0.0   \n",
      "4             0.0             0.0          0.0          0.0   \n",
      "\n",
      "   l1_poor_rep_t_def  interwar  \n",
      "0                NaN       1.0  \n",
      "1                0.0       1.0  \n",
      "2                0.0       1.0  \n",
      "3                0.0       1.0  \n",
      "4                0.0       1.0  \n",
      "\n",
      "[5 rows x 26 columns]\n"
     ]
    }
   ],
   "source": [
    "# Lightweight installer if packages are missing\n",
    "\n",
    "try:\n",
    "    import pandas as _pd\n",
    "    import statsmodels as _sm\n",
    "    import matplotlib as _mpl\n",
    "except Exception:\n",
    "    import sys, subprocess\n",
    "    subprocess.check_call(\n",
    "        [\n",
    "            sys.executable, \"-m\", \"pip\", \"install\", \"-U\",\n",
    "            \"pandas\", \"statsmodels\", \"matplotlib\", \"seaborn\", \"pyreadstat\"\n",
    "        ]\n",
    "    )\n",
    "\n",
    "\n",
    "# Imports\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "from pathlib import Path\n",
    "\n",
    "\n",
    "# Load dataset\n",
    "\n",
    "file_path = Path(\n",
    "    r\"C:\\Users\\rmuhi\\OneDrive\\Desktop\\Reputation_Conflict_Paper_All\\Second Order Reputation\\Replication files\\jpr_replication_data.dta\"\n",
    ")\n",
    "\n",
    "try:\n",
    "    df = pd.read_stata(file_path)\n",
    "except Exception as e:\n",
    "    print(\"Error reading Stata file:\", e)\n",
    "    raise\n",
    "\n",
    "print(\"Data loaded. Shape:\", df.shape)\n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7504f903",
   "metadata": {},
   "source": [
    "#### Figure 3: Pretreatment Trends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "45dc90c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create event_time\n",
    "\n",
    "first_treat = df[df['l1_poor_rep_t_def'] == 1].groupby('id')['year'].min().reset_index()\n",
    "first_treat.columns = ['id', 'first_treat_year']\n",
    "df = pd.merge(df, first_treat, on='id', how='left')\n",
    "df['first_treat_year'] = df['first_treat_year'].fillna(0).astype(int)\n",
    "\n",
    "df['event_time'] = df['year'] - df['first_treat_year']\n",
    "df.loc[df['first_treat_year'] == 0, 'event_time'] = np.nan"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f09d929d",
   "metadata": {},
   "source": [
    "Full sample (no covariates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2d89cd76",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Estimate ATT for each pre-treatment event_time\n",
    "\n",
    "event_times = sorted(df['event_time'].dropna().unique())\n",
    "att_results = []\n",
    "\n",
    "for t in event_times:\n",
    "    if t >= 0:\n",
    "        continue  # Only pre-treatment years\n",
    "\n",
    "    treated = df[df['event_time'] == t]\n",
    "    control = df[(df['first_treat_year'] == 0) & (df['year'].isin(treated['year'].unique()))]\n",
    "\n",
    "    if len(treated) < 5 or len(control) < 5:\n",
    "        continue\n",
    "\n",
    "    temp = pd.concat([treated, control]).copy()\n",
    "\n",
    "    # D = 1 if this unit will be treated in future but hasn't been treated yet\n",
    "    temp['D'] = (temp['first_treat_year'] > temp['year']) & (temp['first_treat_year'] > 0)\n",
    "    temp['D'] = temp['D'].astype(int)\n",
    "\n",
    "    X = sm.add_constant(temp['D']).astype(float)\n",
    "    y = temp['dispute'].astype(float)\n",
    "\n",
    "    model = sm.OLS(y, X, missing='drop').fit()\n",
    "\n",
    "    att_results.append({\n",
    "        'event_time': t,\n",
    "        'att': model.params['D'],\n",
    "        'se': model.bse['D']\n",
    "    })\n",
    "att_df = pd.DataFrame(att_results).sort_values('event_time')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c736c87b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pre-treatment ATT Plot (−30 to −1), with 90% and 95% CIs styled\n",
    "\n",
    "pretrend_df = att_df[(att_df['event_time'] >= -30) & (att_df['event_time'] < 0)]\n",
    "\n",
    "# Extract arrays\n",
    "att = pretrend_df['att'].to_numpy()\n",
    "se = pretrend_df['se'].to_numpy()\n",
    "x = pretrend_df['event_time'].to_numpy()\n",
    "\n",
    "# Confidence bounds\n",
    "ci_low_95 = att - 1.96 * se\n",
    "ci_high_95 = att + 1.96 * se\n",
    "ci_low_90 = att - 1.645 * se\n",
    "ci_high_90 = att + 1.645 * se\n",
    "\n",
    "# Start plot\n",
    "plt.figure(figsize=(6, 4.5))\n",
    "ax = plt.gca()\n",
    "ax.set_facecolor('#f5f5f5')  # Light grey background\n",
    "\n",
    "# 90% CI (lightest shade, outer band)\n",
    "plt.fill_between(x, ci_low_90, ci_high_90,\n",
    "                 color='#c6dbef', alpha=0.4, label='90% CI', linewidth=0)\n",
    "\n",
    "# 95% CI (slightly darker, inner band)\n",
    "plt.fill_between(x, ci_low_95, ci_high_95,\n",
    "                 color='#6baed6', alpha=0.3, label='95% CI', linewidth=0)\n",
    "# ATT line (dark blue)\n",
    "plt.plot(x, att, color='#08306B', lw=1.5, label='ATT Estimate')\n",
    "\n",
    "# Horizontal reference line at 0\n",
    "plt.axhline(0, color='black', linestyle='--', lw=1)\n",
    "\n",
    "# Labels and layout\n",
    "plt.xlabel(\"Event time\", fontsize=11)\n",
    "plt.ylabel(\"Event Study Estimate\", fontsize=11)\n",
    "plt.title(\"Full Sample, No Additional Covariates\", fontsize=12)\n",
    "plt.grid(True, linestyle=':', color='gray', alpha=0.4)\n",
    "plt.ylim(pretrend_df['att'].min() - 0.01, pretrend_df['att'].max() + 0.01)\n",
    "plt.tight_layout()\n",
    "plt.legend(frameon=False, loc='lower right')\n",
    "\n",
    "for spine in ['top', 'right', 'left', 'bottom']:\n",
    "    ax.spines[spine].set_visible(False)\n",
    "\n",
    "# Save at 300 DPI\n",
    "plt.savefig(\n",
    "    r\"C:\\Users\\rmuhi\\OneDrive\\Desktop\\Reputation_Conflict_Paper_All\\Second Order Reputation\\Replication files\\Figure_3_upper_left_panel.png\",\n",
    "    dpi=300,\n",
    "    bbox_inches='tight'\n",
    ")\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5682c22f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                dispute   R-squared:                       0.000\n",
      "Model:                            OLS   Adj. R-squared:                  0.000\n",
      "Method:                 Least Squares   F-statistic:                     39.78\n",
      "Date:                Wed, 10 Dec 2025   Prob (F-statistic):           2.84e-10\n",
      "Time:                        13:03:00   Log-Likelihood:             1.7739e+06\n",
      "No. Observations:             1077992   AIC:                        -3.548e+06\n",
      "Df Residuals:                 1077990   BIC:                        -3.548e+06\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          0.0021    4.9e-05     42.094      0.000       0.002       0.002\n",
      "D              0.0008      0.000      6.307      0.000       0.001       0.001\n",
      "==============================================================================\n",
      "Omnibus:                  2402454.310   Durbin-Watson:                   1.683\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):       9295275145.408\n",
      "Skew:                          21.328   Prob(JB):                         0.00\n",
      "Kurtosis:                     455.909   Cond. No.                         2.83\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "Aggregate ATT (post-treatment): ATT = 0.0008, SE = 0.0001\n"
     ]
    }
   ],
   "source": [
    "# Aggregate ATT Estimation (Post-treatment)\n",
    "\n",
    "# Define post-treatment status and ever-treated dyads\n",
    "df['treated'] = df['first_treat_year'] > 0\n",
    "df['post'] = (df['year'] >= df['first_treat_year']) & (df['first_treat_year'] > 0)\n",
    "\n",
    "# ATT setup: include all dyads that are either treated-post or not yet/never treated\n",
    "agg_df = df[(df['post']) | ((df['first_treat_year'] == 0) | (df['year'] < df['first_treat_year']))].copy()\n",
    "\n",
    "# Create treatment indicator\n",
    "agg_df['D'] = (agg_df['post']) & (agg_df['treated'])\n",
    "agg_df['D'] = agg_df['D'].astype(int)\n",
    "\n",
    "# Drop missing values in DV\n",
    "agg_df = agg_df.dropna(subset=['dispute'])\n",
    "\n",
    "# Run OLS model\n",
    "X = sm.add_constant(agg_df['D']).astype(float)\n",
    "y = agg_df['dispute'].astype(float)\n",
    "model = sm.OLS(y, X).fit()\n",
    "\n",
    "# Output results\n",
    "att1 = model.params['D']\n",
    "se1 = model.bse['D']\n",
    "\n",
    "print(model.summary())\n",
    "print(f\"Aggregate ATT (post-treatment): ATT = {att1:.4f}, SE = {se1:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33bf5408",
   "metadata": {},
   "source": [
    "Full sample (additional covariates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ca2f3d9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Estimate ATT for each pre-treatment event_time (with covariates)\n",
    "\n",
    "covariates = ['ptargdef', 'pchalneu', 'pchaloff', 'capprop', 'majorpower', 'jdem', 's_un_glo', 'ln_distance', 'cold_war']\n",
    "\n",
    "event_times = sorted(df['event_time'].dropna().unique())\n",
    "att_results = []\n",
    "\n",
    "for t in event_times:\n",
    "    if t >= 0:\n",
    "        continue  # Only pre-treatment years\n",
    "\n",
    "    treated = df[df['event_time'] == t]\n",
    "    control = df[(df['first_treat_year'] == 0) & (df['year'].isin(treated['year'].unique()))]\n",
    "\n",
    "    if len(treated) < 5 or len(control) < 5:\n",
    "        continue\n",
    "\n",
    "    temp = pd.concat([treated, control]).copy()\n",
    "\n",
    "    # D = 1 if the unit will be treated later\n",
    "    temp['D'] = (temp['first_treat_year'] > temp['year']) & (temp['first_treat_year'] > 0)\n",
    "    temp['D'] = temp['D'].astype(int)\n",
    "\n",
    "    # Define design matrix with covariates\n",
    "    X_vars = ['D'] + covariates\n",
    "    X = sm.add_constant(temp[X_vars].astype(float))\n",
    "    y = temp['dispute'].astype(float)\n",
    "\n",
    "    model = sm.OLS(y, X, missing='drop').fit()\n",
    "\n",
    "    att_results.append({\n",
    "        'event_time': t,\n",
    "        'att': model.params['D'],\n",
    "        'se': model.bse['D']\n",
    "    })\n",
    "\n",
    "att_df = pd.DataFrame(att_results).sort_values('event_time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "712ac93c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pre-treatment ATT Plot (−30 to −1), with 90% and 95% CIs styled\n",
    "\n",
    "pretrend_df = att_df[(att_df['event_time'] >= -30) & (att_df['event_time'] < 0)]\n",
    "\n",
    "# Extract arrays\n",
    "att = pretrend_df['att'].to_numpy()\n",
    "se = pretrend_df['se'].to_numpy()\n",
    "x = pretrend_df['event_time'].to_numpy()\n",
    "\n",
    "# Confidence bounds\n",
    "ci_low_95 = att - 1.96 * se\n",
    "ci_high_95 = att + 1.96 * se\n",
    "ci_low_90 = att - 1.645 * se\n",
    "ci_high_90 = att + 1.645 * se\n",
    "\n",
    "# Start plot\n",
    "plt.figure(figsize=(6, 4.5))\n",
    "ax = plt.gca()\n",
    "ax.set_facecolor('#f5f5f5')  # Light grey background\n",
    "\n",
    "# 90% CI (lightest shade, outer band)\n",
    "plt.fill_between(x, ci_low_90, ci_high_90,\n",
    "                 color='#c6dbef', alpha=0.4, label='90% CI', linewidth=0)\n",
    "\n",
    "# 95% CI (slightly darker, inner band)\n",
    "plt.fill_between(x, ci_low_95, ci_high_95,\n",
    "                 color='#6baed6', alpha=0.3, label='95% CI', linewidth=0)\n",
    "# ATT line (dark blue)\n",
    "plt.plot(x, att, color='#08306B', lw=1.5, label='ATT Estimate')\n",
    "\n",
    "# Horizontal reference line at 0\n",
    "plt.axhline(0, color='black', linestyle='--', lw=1)\n",
    "\n",
    "# Labels and layout\n",
    "plt.xlabel(\"Event time\", fontsize=11)\n",
    "plt.ylabel(\"Event Study Estimate\", fontsize=11)\n",
    "plt.title(\"Full Sample, Additional Covariates\", fontsize=12)\n",
    "plt.grid(True, linestyle=':', color='gray', alpha=0.4)\n",
    "plt.ylim(pretrend_df['att'].min() - 0.01, pretrend_df['att'].max() + 0.01)\n",
    "plt.tight_layout()\n",
    "plt.legend(frameon=False, loc='lower right')\n",
    "\n",
    "for spine in ['top', 'right', 'left', 'bottom']:\n",
    "    ax.spines[spine].set_visible(False)\n",
    "\n",
    "# Save at 300 DPI\n",
    "plt.savefig(\n",
    "    r\"C:\\Users\\rmuhi\\OneDrive\\Desktop\\Reputation_Conflict_Paper_All\\Second Order Reputation\\Replication files\\Figure_3_upper_right_panel.png\",\n",
    "    dpi=300,\n",
    "    bbox_inches='tight'\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9aa88faa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                dispute   R-squared:                       0.007\n",
      "Model:                            OLS   Adj. R-squared:                  0.007\n",
      "Method:                 Least Squares   F-statistic:                     777.5\n",
      "Date:                Wed, 10 Dec 2025   Prob (F-statistic):               0.00\n",
      "Time:                        13:04:39   Log-Likelihood:             1.7777e+06\n",
      "No. Observations:             1077992   AIC:                        -3.555e+06\n",
      "Df Residuals:                 1077981   BIC:                        -3.555e+06\n",
      "Df Model:                          10                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===============================================================================\n",
      "                  coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------\n",
      "const           0.0323      0.001     55.832      0.000       0.031       0.033\n",
      "D               0.0012      0.000      8.675      0.000       0.001       0.001\n",
      "ptargdef       -0.0009      0.000     -8.710      0.000      -0.001      -0.001\n",
      "pchalneu        0.0022      0.000     10.838      0.000       0.002       0.003\n",
      "pchaloff        0.0016      0.000      6.050      0.000       0.001       0.002\n",
      "capprop         0.0011      0.000      8.703      0.000       0.001       0.001\n",
      "majorpower      0.0061      0.000     42.073      0.000       0.006       0.006\n",
      "jdem           -0.0011      0.000     -7.547      0.000      -0.001      -0.001\n",
      "s_un_glo       -0.0011      0.000     -3.913      0.000      -0.002      -0.001\n",
      "ln_distance    -0.0037   5.58e-05    -66.697      0.000      -0.004      -0.004\n",
      "cold_war        0.0002   9.53e-05      2.412      0.016    4.31e-05       0.000\n",
      "==============================================================================\n",
      "Omnibus:                  2391442.167   Durbin-Watson:                   1.695\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):       9033881466.248\n",
      "Skew:                          21.100   Prob(JB):                         0.00\n",
      "Kurtosis:                     449.482   Cond. No.                         114.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "Aggregate ATT (post-treatment): ATT = 0.0008, SE = 0.0001\n"
     ]
    }
   ],
   "source": [
    "# Aggregate ATT Estimation (Post-treatment)\n",
    "\n",
    "# Define post-treatment status and ever-treated dyads\n",
    "df['treated'] = df['first_treat_year'] > 0\n",
    "df['post'] = (df['year'] >= df['first_treat_year']) & (df['first_treat_year'] > 0)\n",
    "\n",
    "# ATT setup: include all dyads that are either treated-post or not yet/never treated\n",
    "agg_df = df[(df['post']) | ((df['first_treat_year'] == 0) | (df['year'] < df['first_treat_year']))].copy()\n",
    "\n",
    "# Create treatment indicator\n",
    "agg_df['D'] = (agg_df['post']) & (agg_df['treated'])\n",
    "agg_df['D'] = agg_df['D'].astype(int)\n",
    "\n",
    "# Drop missing values in DV\n",
    "agg_df = agg_df.dropna(subset=['dispute'])\n",
    "\n",
    "# Run OLS model\n",
    "X = sm.add_constant(agg_df[['D'] + covariates].astype(float))\n",
    "y = agg_df['dispute'].astype(float)\n",
    "model = sm.OLS(y, X).fit()\n",
    "\n",
    "# Output results\n",
    "att2 = model.params['D']\n",
    "se2 = model.bse['D']\n",
    "\n",
    "print(model.summary())\n",
    "print(f\"Aggregate ATT (post-treatment): ATT = {att1:.4f}, SE = {se1:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61cface2",
   "metadata": {},
   "source": [
    "Restricted sample (no covariates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "72d9e6ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Restrict to ptargdef == 1\n",
    "\n",
    "df = df[df['ptargdef'] == 1].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "55f7e604",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Estimate ATT for each pre-treatment event_time\n",
    "\n",
    "event_times = sorted(df['event_time'].dropna().unique())\n",
    "att_results = []\n",
    "\n",
    "for t in event_times:\n",
    "    if t >= 0:\n",
    "        continue  # Only pre-treatment years\n",
    "\n",
    "    treated = df[df['event_time'] == t]\n",
    "    control = df[(df['first_treat_year'] == 0) & (df['year'].isin(treated['year'].unique()))]\n",
    "\n",
    "    if len(treated) < 5 or len(control) < 5:\n",
    "        continue\n",
    "\n",
    "    temp = pd.concat([treated, control]).copy()\n",
    "\n",
    "    # D = 1 if this unit will be treated in future but hasn't been treated yet\n",
    "    temp['D'] = (temp['first_treat_year'] > temp['year']) & (temp['first_treat_year'] > 0)\n",
    "    temp['D'] = temp['D'].astype(int)\n",
    "\n",
    "    X = sm.add_constant(temp['D']).astype(float)\n",
    "    y = temp['dispute'].astype(float)\n",
    "\n",
    "    model = sm.OLS(y, X, missing='drop').fit()\n",
    "\n",
    "    att_results.append({\n",
    "        'event_time': t,\n",
    "        'att': model.params['D'],\n",
    "        'se': model.bse['D']\n",
    "    })\n",
    "att_df = pd.DataFrame(att_results).sort_values('event_time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "60db666c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 600x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pre-treatment ATT Plot (−30 to −1), with 90% and 95% CIs styled\n",
    "\n",
    "pretrend_df = att_df[(att_df['event_time'] >= -30) & (att_df['event_time'] < 0)]\n",
    "\n",
    "# Extract arrays\n",
    "att = pretrend_df['att'].to_numpy()\n",
    "se = pretrend_df['se'].to_numpy()\n",
    "x = pretrend_df['event_time'].to_numpy()\n",
    "\n",
    "# Confidence bounds\n",
    "ci_low_95 = att - 1.96 * se\n",
    "ci_high_95 = att + 1.96 * se\n",
    "ci_low_90 = att - 1.645 * se\n",
    "ci_high_90 = att + 1.645 * se\n",
    "\n",
    "# Start plot\n",
    "plt.figure(figsize=(6, 4.5))\n",
    "ax = plt.gca()\n",
    "ax.set_facecolor('#f5f5f5')  # Light grey background\n",
    "\n",
    "# 90% CI (lightest shade, outer band)\n",
    "plt.fill_between(x, ci_low_90, ci_high_90,\n",
    "                 color='#c6dbef', alpha=0.4, label='90% CI', linewidth=0)\n",
    "\n",
    "# 95% CI (slightly darker, inner band)\n",
    "plt.fill_between(x, ci_low_95, ci_high_95,\n",
    "                 color='#6baed6', alpha=0.3, label='95% CI', linewidth=0)\n",
    "# ATT line (dark blue)\n",
    "plt.plot(x, att, color='#08306B', lw=1.5, label='ATT Estimate')\n",
    "\n",
    "# Horizontal reference line at 0\n",
    "plt.axhline(0, color='black', linestyle='--', lw=1)\n",
    "\n",
    "# Labels and layout\n",
    "plt.xlabel(\"Event time\", fontsize=11)\n",
    "plt.ylabel(\"Event Study Estimate\", fontsize=11)\n",
    "plt.title(\"Restricted Sample, No Additional Covariates\", fontsize=12)\n",
    "plt.grid(True, linestyle=':', color='gray', alpha=0.4)\n",
    "plt.ylim(pretrend_df['att'].min() - 0.01, pretrend_df['att'].max() + 0.01)\n",
    "plt.tight_layout()\n",
    "plt.legend(frameon=False, loc='lower right')\n",
    "\n",
    "for spine in ['top', 'right', 'left', 'bottom']:\n",
    "    ax.spines[spine].set_visible(False)\n",
    "\n",
    "# Save at 300 DPI\n",
    "plt.savefig(\n",
    "    r\"C:\\Users\\rmuhi\\OneDrive\\Desktop\\Reputation_Conflict_Paper_All\\Second Order Reputation\\Replication files\\Figure_3_bottom_left_panel.png\",\n",
    "    dpi=300,\n",
    "    bbox_inches='tight'\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "004f3e2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                dispute   R-squared:                       0.000\n",
      "Model:                            OLS   Adj. R-squared:                  0.000\n",
      "Method:                 Least Squares   F-statistic:                     52.78\n",
      "Date:                Wed, 10 Dec 2025   Prob (F-statistic):           3.73e-13\n",
      "Time:                        13:05:44   Log-Likelihood:             1.0114e+06\n",
      "No. Observations:              585467   AIC:                        -2.023e+06\n",
      "Df Residuals:                  585465   BIC:                        -2.023e+06\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          0.0016    6.6e-05     24.267      0.000       0.001       0.002\n",
      "D              0.0009      0.000      7.265      0.000       0.001       0.001\n",
      "==============================================================================\n",
      "Omnibus:                  1352853.671   Durbin-Watson:                   1.658\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):       7021774553.643\n",
      "Skew:                          23.162   Prob(JB):                         0.00\n",
      "Kurtosis:                     537.506   Cond. No.                         2.45\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "Aggregate ATT (post-treatment): ATT = 0.0008, SE = 0.0001\n"
     ]
    }
   ],
   "source": [
    "# Aggregate ATT Estimation (Post-treatment)\n",
    "\n",
    "# Define post-treatment status and ever-treated dyads\n",
    "df['treated'] = df['first_treat_year'] > 0\n",
    "df['post'] = (df['year'] >= df['first_treat_year']) & (df['first_treat_year'] > 0)\n",
    "\n",
    "# ATT setup: include all dyads that are either treated-post or not yet/never treated\n",
    "agg_df = df[(df['post']) | ((df['first_treat_year'] == 0) | (df['year'] < df['first_treat_year']))].copy()\n",
    "\n",
    "# Create treatment indicator\n",
    "agg_df['D'] = (agg_df['post']) & (agg_df['treated'])\n",
    "agg_df['D'] = agg_df['D'].astype(int)\n",
    "\n",
    "# Drop missing values in DV\n",
    "agg_df = agg_df.dropna(subset=['dispute'])\n",
    "\n",
    "# Run OLS model\n",
    "X = sm.add_constant(agg_df['D']).astype(float)\n",
    "y = agg_df['dispute'].astype(float)\n",
    "model = sm.OLS(y, X).fit()\n",
    "\n",
    "# Output results\n",
    "att3 = model.params['D']\n",
    "se3 = model.bse['D']\n",
    "\n",
    "print(model.summary())\n",
    "print(f\"Aggregate ATT (post-treatment): ATT = {att1:.4f}, SE = {se1:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a354ad7e",
   "metadata": {},
   "source": [
    "Restricted sample (additional covariates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "999f4424",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Estimate ATT for each pre-treatment event_time (with covariates)\n",
    "\n",
    "covariates2 = ['pchalneu', 'pchaloff', 'capprop', 'majorpower', 'jdem', 's_un_glo', 'ln_distance', 'cold_war']\n",
    "\n",
    "event_times = sorted(df['event_time'].dropna().unique())\n",
    "att_results = []\n",
    "\n",
    "for t in event_times:\n",
    "    if t >= 0:\n",
    "        continue  # Only pre-treatment years\n",
    "\n",
    "    treated = df[df['event_time'] == t]\n",
    "    control = df[(df['first_treat_year'] == 0) & (df['year'].isin(treated['year'].unique()))]\n",
    "\n",
    "    if len(treated) < 5 or len(control) < 5:\n",
    "        continue\n",
    "\n",
    "    temp = pd.concat([treated, control]).copy()\n",
    "\n",
    "    # D = 1 if the unit will be treated later\n",
    "    temp['D'] = (temp['first_treat_year'] > temp['year']) & (temp['first_treat_year'] > 0)\n",
    "    temp['D'] = temp['D'].astype(int)\n",
    "\n",
    "    # Define design matrix with covariates\n",
    "    X_vars = ['D'] + covariates2\n",
    "    X = sm.add_constant(temp[X_vars].astype(float))\n",
    "    y = temp['dispute'].astype(float)\n",
    "\n",
    "    model = sm.OLS(y, X, missing='drop').fit()\n",
    "\n",
    "    att_results.append({\n",
    "        'event_time': t,\n",
    "        'att': model.params['D'],\n",
    "        'se': model.bse['D']\n",
    "    })\n",
    "\n",
    "att_df = pd.DataFrame(att_results).sort_values('event_time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fa94cdf9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 600x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pre-treatment ATT Plot (−30 to −1), with 90% and 95% CIs styled\n",
    "\n",
    "pretrend_df = att_df[(att_df['event_time'] >= -30) & (att_df['event_time'] < 0)]\n",
    "\n",
    "# Extract arrays\n",
    "att = pretrend_df['att'].to_numpy()\n",
    "se = pretrend_df['se'].to_numpy()\n",
    "x = pretrend_df['event_time'].to_numpy()\n",
    "\n",
    "# Confidence bounds\n",
    "ci_low_95 = att - 1.96 * se\n",
    "ci_high_95 = att + 1.96 * se\n",
    "ci_low_90 = att - 1.645 * se\n",
    "ci_high_90 = att + 1.645 * se\n",
    "\n",
    "# Start plot\n",
    "plt.figure(figsize=(6, 4.5))\n",
    "ax = plt.gca()\n",
    "ax.set_facecolor('#f5f5f5')  # Light grey background\n",
    "\n",
    "# 90% CI (lightest shade, outer band)\n",
    "plt.fill_between(x, ci_low_90, ci_high_90,\n",
    "                 color='#c6dbef', alpha=0.4, label='90% CI', linewidth=0)\n",
    "\n",
    "# 95% CI (slightly darker, inner band)\n",
    "plt.fill_between(x, ci_low_95, ci_high_95,\n",
    "                 color='#6baed6', alpha=0.3, label='95% CI', linewidth=0)\n",
    "# ATT line (dark blue)\n",
    "plt.plot(x, att, color='#08306B', lw=1.5, label='ATT Estimate')\n",
    "\n",
    "# Horizontal reference line at 0\n",
    "plt.axhline(0, color='black', linestyle='--', lw=1)\n",
    "\n",
    "# Labels and layout\n",
    "plt.xlabel(\"Event time\", fontsize=11)\n",
    "plt.ylabel(\"Event Study Estimate\", fontsize=11)\n",
    "plt.title(\"Restricted Sample, Additional Covariates\", fontsize=12)\n",
    "plt.grid(True, linestyle=':', color='gray', alpha=0.4)\n",
    "plt.ylim(pretrend_df['att'].min() - 0.01, pretrend_df['att'].max() + 0.01)\n",
    "plt.tight_layout()\n",
    "plt.legend(frameon=False, loc='lower right')\n",
    "\n",
    "for spine in ['top', 'right', 'left', 'bottom']:\n",
    "    ax.spines[spine].set_visible(False)\n",
    "\n",
    "# Save at 300 DPI\n",
    "plt.savefig(\n",
    "    r\"C:\\Users\\rmuhi\\OneDrive\\Desktop\\Reputation_Conflict_Paper_All\\Second Order Reputation\\Replication files\\Figure_3_bottom_right_panel.png\",\n",
    "    dpi=300,\n",
    "    bbox_inches='tight'\n",
    ")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "94dda7df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                dispute   R-squared:                       0.007\n",
      "Model:                            OLS   Adj. R-squared:                  0.007\n",
      "Method:                 Least Squares   F-statistic:                     431.7\n",
      "Date:                Wed, 10 Dec 2025   Prob (F-statistic):               0.00\n",
      "Time:                        13:06:44   Log-Likelihood:             1.0133e+06\n",
      "No. Observations:              585467   AIC:                        -2.027e+06\n",
      "Df Residuals:                  585457   BIC:                        -2.026e+06\n",
      "Df Model:                           9                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===============================================================================\n",
      "                  coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------\n",
      "const           0.0292      0.001     39.800      0.000       0.028       0.031\n",
      "D               0.0008      0.000      6.503      0.000       0.001       0.001\n",
      "pchalneu        0.0024      0.000      9.385      0.000       0.002       0.003\n",
      "pchaloff        0.0013      0.000      4.014      0.000       0.001       0.002\n",
      "capprop         0.0004      0.000      2.662      0.008       0.000       0.001\n",
      "majorpower      0.0057      0.000     32.026      0.000       0.005       0.006\n",
      "jdem           -0.0008      0.000     -4.398      0.000      -0.001      -0.000\n",
      "s_un_glo       -0.0014      0.000     -4.409      0.000      -0.002      -0.001\n",
      "ln_distance    -0.0034   7.36e-05    -46.293      0.000      -0.004      -0.003\n",
      "cold_war        0.0006      0.000      4.538      0.000       0.000       0.001\n",
      "==============================================================================\n",
      "Omnibus:                  1347306.463   Durbin-Watson:                   1.669\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):       6841129738.153\n",
      "Skew:                          22.935   Prob(JB):                         0.00\n",
      "Kurtosis:                     530.573   Cond. No.                         115.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "Aggregate ATT (post-treatment): ATT = 0.0008, SE = 0.0001\n"
     ]
    }
   ],
   "source": [
    "# Aggregate ATT Estimation (Post-treatment)\n",
    "\n",
    "# Define post-treatment status and ever-treated dyads\n",
    "df['treated'] = df['first_treat_year'] > 0\n",
    "df['post'] = (df['year'] >= df['first_treat_year']) & (df['first_treat_year'] > 0)\n",
    "\n",
    "# ATT setup: include all dyads that are either treated-post or not yet/never treated\n",
    "agg_df = df[(df['post']) | ((df['first_treat_year'] == 0) | (df['year'] < df['first_treat_year']))].copy()\n",
    "\n",
    "# Create treatment indicator\n",
    "agg_df['D'] = (agg_df['post']) & (agg_df['treated'])\n",
    "agg_df['D'] = agg_df['D'].astype(int)\n",
    "\n",
    "# Drop missing values in DV\n",
    "agg_df = agg_df.dropna(subset=['dispute'])\n",
    "\n",
    "# Run OLS model\n",
    "X = sm.add_constant(agg_df[['D'] + covariates2].astype(float))\n",
    "y = agg_df['dispute'].astype(float)\n",
    "model = sm.OLS(y, X).fit()\n",
    "\n",
    "# Output results\n",
    "att4 = model.params['D']\n",
    "se4 = model.bse['D']\n",
    "\n",
    "print(model.summary())\n",
    "print(f\"Aggregate ATT (post-treatment): ATT = {att1:.4f}, SE = {se1:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a457232",
   "metadata": {},
   "source": [
    "#### Figure 4. Average Treatment Effects on the Treated "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6a3eff19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x320 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "# Step 1: Prepare the data\n",
    "group1 = pd.DataFrame({\n",
    "    'Model': ['Full Sample', 'Restricted Sample'],\n",
    "    'att': [att1, att3],\n",
    "    'se': [se1, se3]\n",
    "})\n",
    "group1['ci_low'] = group1['att'] - 1.96 * group1['se']\n",
    "group1['ci_high'] = group1['att'] + 1.96 * group1['se']\n",
    "\n",
    "group2 = pd.DataFrame({\n",
    "    'Model': ['Full Sample', 'Restricted Sample'],\n",
    "    'att': [att2, att4],\n",
    "    'se': [se2, se4]\n",
    "})\n",
    "group2['ci_low'] = group2['att'] - 1.96 * group2['se']\n",
    "group2['ci_high'] = group2['att'] + 1.96 * group2['se']\n",
    "\n",
    "# Step 2: Positions\n",
    "y_positions = [1, 0]\n",
    "dot_color = '#e34a33'\n",
    "\n",
    "# Step 3: Subplots\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 3.2), sharey=True)\n",
    "\n",
    "# === Left plot: No Covariates ===\n",
    "ax1.set_facecolor('#f9f9f9')\n",
    "for spine in ['top', 'right', 'left', 'bottom']:\n",
    "    ax1.spines[spine].set_visible(False)\n",
    "\n",
    "ax1.errorbar(group1['att'], y_positions,\n",
    "             xerr=1.96 * group1['se'],\n",
    "             fmt='o', color=dot_color, ecolor=dot_color,\n",
    "             elinewidth=1, capsize=2, markersize=6)\n",
    "\n",
    "ax1.axvline(0, color='black', linestyle='--', linewidth=1)\n",
    "ax1.set_xlabel(\"ATT\", fontsize=10)\n",
    "ax1.set_title(\"No Covariates\", fontsize=11)\n",
    "ax1.set_xlim(group1['ci_low'].min() - 0.0015, group1['ci_high'].max() + 0.0005)\n",
    "ax1.set_ylim(-0.5, 1.5)\n",
    "ax1.set_yticks([])  # Hide tick system\n",
    "ax1.grid(True, linestyle=':', linewidth=0.7, alpha=0.6)\n",
    "\n",
    "for y, label in zip(y_positions, group1['Model']):\n",
    "    ax1.text(group1['ci_low'].min() - 0.0016, y, label,\n",
    "             va='center', ha='right', fontsize=10)\n",
    "\n",
    "\n",
    "# === Right plot: With Covariates ===\n",
    "ax2.set_facecolor('#f9f9f9')\n",
    "for spine in ['top', 'right', 'left', 'bottom']:\n",
    "    ax2.spines[spine].set_visible(False)\n",
    "\n",
    "ax2.errorbar(group2['att'], y_positions,\n",
    "             xerr=1.96 * group2['se'],\n",
    "             fmt='o', color=dot_color, ecolor=dot_color,\n",
    "             elinewidth=1, capsize=2, markersize=6)\n",
    "\n",
    "ax2.axvline(0, color='black', linestyle='--', linewidth=1)\n",
    "ax2.set_xlabel(\"ATT\", fontsize=10)\n",
    "ax2.set_title(\"With Covariates\", fontsize=11)\n",
    "ax2.set_xlim(group2['ci_low'].min() - 0.0016, group2['ci_high'].max() + 0.0006)\n",
    "ax2.set_ylim(-0.5, 1.5)\n",
    "ax2.set_yticks([])  # Hide tick system\n",
    "ax2.grid(True, linestyle=':', linewidth=0.7, alpha=0.6)\n",
    "\n",
    "# Final layout\n",
    "plt.suptitle(\"\", fontsize=12)\n",
    "plt.tight_layout(rect=[0, 0, 1, 0.93])\n",
    "# Save at 300 DPI\n",
    "plt.savefig(\n",
    "    r\"C:\\Users\\rmuhi\\OneDrive\\Desktop\\Reputation_Conflict_Paper_All\\Second Order Reputation\\Replication files\\Figure_4.png\",\n",
    "    dpi=300,\n",
    "    bbox_inches='tight'\n",
    ")\n",
    "plt.show()"
   ]
  }
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